melihemin/qwen2.5-0.5b-text2sql-full

Model Card for Qwen2.5-0.5B Text-to-SQL

Model Summary

This model converts natural language questions into SQL queries.
It is a fine-tuned version of Qwen2.5-0.5B, adapted specifically for the Text-to-SQL task using the LoRA (Low-Rank Adaptation) method.

The model is designed to be lightweight, efficient, and suitable for local experimentation and educational purposes.

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Model Details

Model Description

• Developed by: Melih Emin
• Model type: Causal Language Model (Text-to-SQL)
• Language(s): English
• License: Apache 2.0
• Finetuned from model: Qwen/Qwen2.5-0.5B
• Fine-tuning method: LoRA (Low-Rank Adaptation)

This model was fine-tuned as part of a Generative Artificial Intelligence course assignment.
The primary goal was to explore parameter-efficient fine-tuning techniques on limited local hardware.

Model Sources

• Base Model: https://huggingface.co/Qwen/Qwen2.5-0.5B
• Repository: https://huggingface.co/melihemin/qwen2.5-0.5b-text2sql-full

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Uses

Direct Use

• Converting English questions into SQL queries
• Educational demonstrations of Text-to-SQL systems
• Local experimentation with small language models

Downstream Use

• Can be integrated into database query assistants
• Can serve as a baseline for more advanced Text-to-SQL systems
• Further fine-tuning with schema-specific datasets

Out-of-Scope Use

• Production-grade database querying without validation
• Complex multi-database or highly nested SQL queries
• Security-critical or sensitive data environments

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Bias, Risks, and Limitations

• The model may generate syntactically valid but semantically incorrect SQL
• It does not perform schema validation
• Performance depends heavily on prompt structure
• Trained on a limited dataset and may not generalize to unseen schemas

Recommendations

• Always validate generated SQL before execution
• Use schema-aware prompting for better results
• Do not use directly in production without safeguards

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How to Get Started with the Model

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "melihemin/qwen2.5-0.5b-text2sql-full"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

prompt = """### Question:
How many heads of the departments are older than 56?

### SQL:
"""

inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=128)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))